Arrhenius plots convert temperature-dependent rate constants into a straight-line fit that yields activation energy. Plotivy creates ln(k) versus 1/T figures with annotations that work for chemistry, catalysis, and materials science.
If your rate constant changes with temperature, an Arrhenius plot is the fastest way to quantify the barrier behind it. The slope gives activation energy, the intercept carries the pre-exponential factor, and the linearity tells you whether a single-process model is a reasonable fit.
Extract activation energy from rate constants across a temperature series.
Compare how different catalysts shift the energy barrier.
Track thermal degradation, diffusion, or aging behavior.
Check whether the data follow a single straight-line regime or need a segmented model.
Show ln(k) against 1/T with a fit line that is easy to cite in a manuscript or report.
Surface the slope-derived Ea value on the figure so readers do not need to infer it from the code.
Keep the axes and annotations clean enough for reaction kinetics, catalysis, or thermal analysis.
Use Python output that can be tuned for real datasets without rebuilding the plot from scratch.
This example takes rate constants at six temperatures, fits ln(k) versus 1/T, and annotates the activation energy and R-squared value directly on the chart.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Charts that often appear beside kinetics and materials analysis figures
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced styling
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],Convert your temperature-dependent kinetics into a clear Arrhenius figure with a regression line, Ea annotation, and publication-ready formatting.